OpenCV3.1 SIFT使用

OpenCV3对OpenCV的模块进行了调整，将开发中与nofree模块放在了OpenCV_contrib中（包含SIFT），gitHub上的官方项目分成了两个，opencv 与 opencv_contrib。所以，要使用sift接口需在opencv3.1基础上，再安装opencv_contrib。本文主要记录如何安装opencv_contrib，配置Xcode，sift接口的用法。
环境：OSX + Xcode + OpenCV3.1

install opencv_contrib

download contrib source code , follow README.md to install

$ cd 
     
      $ cmake -DOPENCV_EXTRA_MODULES_PATH=
      
       /modules 
       
        $ make -j5$ sudo make install

Where <opencv_build_directory> and <opencv_source_directory> is directory in opencv3.1

configuration Xcode

like

pro_name Build Setting > Search Paths

/usr/local/lib

/usr/local/include

pro_name Build Setting >Other Linker Flags

-lopencv_stitching -lopencv_superres -lopencv_videostab -lopencv_aruco -lopencv_bgsegm -lopencv_bioinspired -lopencv_ccalib -lopencv_dnn -lopencv_dpm -lopencv_fuzzy -lopencv_line_descriptor -lopencv_optflow -lopencv_plot -lopencv_reg -lopencv_saliency -lopencv_stereo -lopencv_structured_light -lopencv_rgbd -lopencv_surface_matching -lopencv_tracking -lopencv_datasets -lopencv_text -lopencv_face -lopencv_xfeatures2d -lopencv_shape -lopencv_video -lopencv_ximgproc -lopencv_calib3d -lopencv_features2d -lopencv_flann -lopencv_xobjdetect -lopencv_objdetect -lopencv_ml -lopencv_xphoto -lippicv -lopencv_highgui -lopencv_videoio -lopencv_imgcodecs -lopencv_photo -lopencv_imgproc -lopencv_core

sample of sift

sample in (souce_dir)/samples/cpp/tutorial_code/xfeatures2D/LATCH_match.cpp or bellow

#include "opencv2/xfeatures2d.hpp"// // now, you can no more create an instance on the 'stack', like in the tutorial// (yea, noticed for a fix/pr).// you will have to use cv::Ptr all the way down://cv::Ptr
       
         f2d = xfeatures2d::SIFT::create();//cv::Ptr
        
          f2d = xfeatures2d::SURF::create();//cv::Ptr
         
           f2d = ORB::create();// you get the picture, i hope..//-- Step 1: Detect the keypoints:std::vector
          
            keypoints_1, keypoints_2;    f2d->detect( img_1, keypoints_1 );f2d->detect( img_2, keypoints_2 );//-- Step 2: Calculate descriptors (feature vectors) Mat descriptors_1, descriptors_2;    f2d->compute( img_1, keypoints_1, descriptors_1 );f2d->compute( img_2, keypoints_2, descriptors_2 );//-- Step 3: Matching descriptor vectors using BFMatcher :BFMatcher matcher;std::vector< DMatch > matches;matcher.match( descriptors_1, descriptors_2, matches );